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%Evaluations Introduction
To evaluate the performances of the implemented algorithms, a dataset of handwritten digits \cite{OpticalDigitsDataset} has been employed.
This dataset consists of a training set of 3823 records of 64 features each. Every feature represents the sum of a $4\times4$ block of pixel binary intensity values, therefore it can take values in the range $\lbrace 0 \ldots 16\rbrace$. The classes of the records are the ten numeric digits in the range $\lbrace 0\ldots 9\rbrace$. During the tree building phase all the implemented algorithms employ the bagging technique \cite{bagging} yielding as many training sets as the number of trees of the forest. This implies randomly sampling a fixed number of elements, 1000 for this specific case, with replacement from the pool of 3823 records of the original dataset to build many smaller training sets.
The classification task has been performed on a test set of 1797 data records not included in the original training set. The machine used to carry out all the benchmarks is equipped with an Intel Core i7 720QM Processor with a clock frequency of 1.60 Ghz  and 4 GB of RAM memory. The capabilities offered by the multi-core architecture have not been exploited since the software runs on a single thread.
